Mental fatigue results in feelings of tiredness and decreases task performance and efficiency. It can be caused by cognitive underload (passive fatigue) or overload (active fatigue). Although fatigue detection models have been developed using electroencephalogram (EEG) recordings, it is unclear whether their predictions (derived from one task) are applicable to different tasks. We investigated if an EEG-based fatigue model previously trained on a passive fatigue-inducing driving task, can predict fatigue during an active fatigue-inducing task. We collected EEG while subjects performed a target-hitting task as a non-fatigued baseline, followed by mental arithmetic tasks of increasing complexity and mental load. Overall, the fatigue score from the passive fatigue model was significantly correlated with self-reported fatigue and sleepiness, which increased over the course of the experiment. The fatigue score was higher during simple mental arithmetic than the target-hitting task, demonstrating that the model can successfully differentiate non-fatigued and passive/low mental load situations. However, the fatigue score was higher during the simple (low mental load) than the complex (high mental load) mental arithmetic task, showing that the model cannot accurately detect active fatigue. Future fatigue models should be trained on a variety of both active and passive fatigue-inducing tasks to be more generalizable.